The fabrication of integrated circuits is an extremely complex process that may involve hundreds of individual operations. In view of the device and interconnect densities required in present day integrated circuits, it is imperative that the manufacturing processes be carried out with utmost precision and in a way that minimizes defects. For reliable operation, the electrical characteristics of the circuits must be kept within carefully controlled limits, which imply a high degree of control over the myriad of operations and fabrication processes. For example, in the photoresist and photomask operations, the presence of contaminants such as dust, minute scratches and other imperfections in the patterns on the photomasks can produce defective patterns on the semiconductor wafers, resulting in defective integrated circuits. Further, defects can be introduced in the circuits during the diffusion operations themselves.
The figure of merit of a semiconductor manufacturing facility is the sort yield obtained by electrically probing the completed devices. However, due to the multitude and complexity of process steps and their associated cost, it is desirable to detect problems early in the design phase in order to correct them, and to predict the yield in order to plan, during the manufacturing phase, wafer starts appropriately. Currently, designers use yield prediction software to decide which design layout alternative will produce a better yield, and thus be printed, and to decide how many wafers to put inline, i.e., adjust the number of wafer starts for production per product based on real inline data to meet the yielding die commitments.
During the design phase, existing software predicts yield based on the wafer design and fabrication defect data using a statistical critical area calculation. The problem with the existing yield prediction software lies in the hidden assumption that the defect distribution is random over the die and the likelihood of a defect to occur on different design elements is the same. With older technology nodes (90 nm and above), where most of the defects were actual random defects, these assumptions could hold true. However, with new technology nodes, where the number of systematic defects, i.e., defects due to non-random errors that are conditioned by the specifics of a design layout or the equipment, has increased significantly, the typical yield prediction software still distributes the defects randomly, even though the defects may occur in specific design elements.
During the manufacturing phase, one way to plan wafer starts appropriately is by utilizing inline inspection tools to detect process defects on the wafers in process. These tools are typically optical microscopes, but of late electron-beam inspection tools have been introduced for certain critical layers. The defects detected by inspection tools are referred to as ‘visual defects’. Not all visual defects will cause an electrical fail. Conversely, not all yield loss can be attributed to visual defects. It has been a goal in the industry to be able to predict the yield loss due to visual defects. The methodology most commonly used is the Kill Ratio method that empirically deduces the fail probabilities of different defect classes by overlaying inline defects with sorted yield data. It is performed on an initial training set, and then applied to wafers in process. This method either requires significant manual classification for the learning set or relies on inspection tool classification that typically has low accuracy and purity. In addition, when new defect classes arise, the learning phase has to be repeated.